Rows: 152,084
Columns: 23
$ cas <chr> "Arequipa", "Arequipa", "Arequipa", "Arequipa", "Arequipa"…
$ sex <fct> Masculino, Masculino, Masculino, Masculino, Masculino, Fem…
$ age <dbl> 15, 15, 15, 15, 15, 18, 15, 16, NA, NA, NA, 22, 21, NA, 22…
$ hta <dbl> 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, NA, 0, 1, 0, 1, …
$ dm <dbl> 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1,…
$ crea <dbl> 0.99, 1.20, 0.97, 0.91, 0.81, 0.76, 0.88, 1.20, 0.75, 0.92…
$ ckd_stage <fct> Stages 1-2 y 5, Stages 1-2 y 5, Stages 1-2 y 5, Stages 1-2…
$ ckd_stage2 <fct> Stages 1-3 y 5, Stages 1-3 y 5, Stages 1-3 y 5, Stages 1-3…
$ eGFR_ckdepi <dbl> 113.08736, 89.62041, 115.91243, 125.21489, 132.51473, 114.…
$ acr <dbl> NA, NA, NA, NA, NA, NA, 0.2522603, NA, NA, NA, NA, NA, 392…
$ urine_album <dbl> NA, NA, NA, NA, NA, NA, 22.60, NA, NA, NA, NA, NA, 196.94,…
$ urine_crea <dbl> NA, NA, NA, NA, NA, NA, 89.5900, NA, 278.1400, 392.2000, 1…
$ time5y <dbl> 5.0000000, 5.0000000, 5.0000000, 5.0000000, 5.0000000, 4.0…
$ eventd5y <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ grf_cat <fct> G1, G2, G1, G1, G1, G1, G1, G2, NA, NA, NA, G3b, G1, NA, G…
$ acr_cat <fct> NA, NA, NA, NA, NA, NA, A1, NA, NA, NA, NA, NA, A3, NA, A3…
$ ckd_class <fct> NA, NA, NA, NA, NA, NA, Low risk, NA, NA, NA, NA, NA, High…
$ death2y <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ eventd2ylab <chr> "Alive w/o Kidney Failure", "Alive w/o Kidney Failure", "A…
$ death5y <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ eventd5ylab <chr> "Alive w/o Kidney Failure", "Alive w/o Kidney Failure", "A…
$ eventd <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ time <dbl> 7.4934976, 7.6933607, 7.4934976, 7.9041752, 7.9041752, 4.0…